{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Xr_XRXYZSCu6"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.layers import (\n",
        "    Input, Dense, Dropout, BatchNormalization, MultiHeadAttention, LayerNormalization, Flatten, TimeDistributed\n",
        ")\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.signal import savgol_filter\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load datasets\n",
        "train_file_path = '/content/drive/MyDrive/project-3/data/mitbih_train.csv'\n",
        "test_file_path = '/content/drive/MyDrive/project-3/data/mitbih_test.csv'\n",
        "\n",
        "# Load dataset\n",
        "def load_data(file_path):\n",
        "    data = pd.read_csv(file_path)\n",
        "    return data\n",
        "\n",
        "train_data = load_data(train_file_path)\n",
        "test_data = load_data(test_file_path)"
      ],
      "metadata": {
        "id": "JYhfBTPGwye0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Separate features and labels\n",
        "X_train_raw = train_data.iloc[:, :-1].values\n",
        "y_train = train_data.iloc[:, -1].values\n",
        "X_test_raw = test_data.iloc[:, :-1].values\n",
        "y_test = test_data.iloc[:, -1].values"
      ],
      "metadata": {
        "id": "Dz_JDUNYxItv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# One-hot encode labels\n",
        "y_train = tf.keras.utils.to_categorical(y_train, num_classes=5)\n",
        "y_test = tf.keras.utils.to_categorical(y_test, num_classes=5)"
      ],
      "metadata": {
        "id": "J4QzoMi6xMKM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Apply Savitzky-Golay filtering for signal smoothing\n",
        "def preprocess_data(X):\n",
        "    smoothed_X = np.array([savgol_filter(signal, window_length=5, polyorder=2) if len(signal) >= 5 else signal for signal in X])\n",
        "    return smoothed_X\n",
        "\n",
        "X_train_smoothed = preprocess_data(X_train_raw)\n",
        "X_test_smoothed = preprocess_data(X_test_raw)\n",
        "\n",
        "# Standardize the data\n",
        "scaler = StandardScaler()\n",
        "X_train_standardized = X_train_standardized.reshape(X_train_standardized.shape[0], X_train_standardized.shape[1], 1)\n",
        "X_test_standardized = X_test_standardized.reshape(X_test_standardized.shape[0], X_test_standardized.shape[1], 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200
        },
        "id": "ijOikAx2xrhr",
        "outputId": "99408af3-20c3-47df-87c7-4449bb558be8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'X_train_standardized' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-9-f41b844b765b>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;31m# Standardize the data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mscaler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mStandardScaler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mX_train_standardized\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX_train_standardized\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train_standardized\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_train_standardized\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0mX_test_standardized\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX_test_standardized\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test_standardized\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test_standardized\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'X_train_standardized' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define Transformer model for multi-class classification\n",
        "def create_transformer_model(input_shape, num_classes, num_heads=4, key_dim=64, num_layers=3):\n",
        "    inputs = Input(shape=(input_shape[0], input_shape[1]))\n",
        "    x = TimeDistributed(Dense(64, activation='relu'))(inputs)\n",
        "\n",
        "    for _ in range(num_layers):\n",
        "        attn_output = MultiHeadAttention(num_heads=num_heads, key_dim=key_dim)(x, x)\n",
        "        x = LayerNormalization(epsilon=1e-6)(x + attn_output)\n",
        "        feed_forward = Dense(64, activation='relu')(x)\n",
        "        x = LayerNormalization(epsilon=1e-6)(x + feed_forward)\n",
        "\n",
        "    x = Flatten()(x)\n",
        "    x = Dense(128, activation='relu')(x)\n",
        "    x = Dropout(0.5)(x)\n",
        "    outputs = Dense(num_classes, activation='softmax')(x)\n",
        "\n",
        "    optimizer = tf.keras.optimizers.Adam(learning_rate=0.0005)\n",
        "    model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "    return model"
      ],
      "metadata": {
        "id": "2iPieLMrxxcT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_classes = 5\n",
        "model = create_transformer_model(input_shape=X_train_standardized.shape[1:], num_classes=num_classes)\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 182
        },
        "id": "JwRDR0NMyBiM",
        "outputId": "5393145d-0ace-476e-d250-b3b2c9c73eaf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'create_transformer_model' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-3-155a30062bcd>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mnum_classes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcreate_transformer_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_shape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mX_train_standardized\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_classes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_classes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'create_transformer_model' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model\n",
        "history = model.fit(\n",
        "    X_train_standardized, y_train,\n",
        "    validation_data=(X_test_standardized, y_test),\n",
        "    epochs=50,\n",
        "    batch_size=32,\n",
        "    callbacks=[\n",
        "        tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True),\n",
        "        tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=3, min_lr=1e-6)\n",
        "    ]\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 219
        },
        "id": "PlGz36BEyE4D",
        "outputId": "f2fc10eb-7fc6-4025-e4fc-6b691fc76a18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'model' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-6a4dbe0a8584>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Train the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m history = model.fit(\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0mX_train_standardized\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test_standardized\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model\n",
        "loss, accuracy = model.evaluate(X_test_standardized, y_test)\n",
        "print(f\"Test Accuracy: {accuracy:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tUpcrIwFz0fw",
        "outputId": "01ac9acc-3b70-4327-fca9-4a2fd4f66b66"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m685/685\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9951 - loss: 0.0234\n",
            "Test Accuracy: 0.98\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate predictions\n",
        "y_pred = np.argmax(model.predict(X_test_standardized), axis=1)\n",
        "y_true = np.argmax(y_test, axis=1)\n",
        "\n",
        "# Classification report\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_true, y_pred, target_names=[str(i) for i in range(5)]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qjFUi5LB5CvR",
        "outputId": "889f8af5-98a6-4ffa-faf3-b38ed4da7a69"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m685/685\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m27s\u001b[0m 22ms/step\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      1.00      0.99     18117\n",
            "           1       0.95      0.65      0.77       556\n",
            "           2       0.97      0.92      0.94      1448\n",
            "           3       0.85      0.71      0.77       162\n",
            "           4       0.99      0.98      0.98      1608\n",
            "\n",
            "    accuracy                           0.98     21891\n",
            "   macro avg       0.95      0.85      0.89     21891\n",
            "weighted avg       0.98      0.98      0.98     21891\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Confusion matrix\n",
        "conf_matrix = confusion_matrix(y_true, y_pred)\n",
        "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=range(5), yticklabels=range(5))\n",
        "plt.title('Confusion Matrix')\n",
        "plt.ylabel('True Label')\n",
        "plt.xlabel('Predicted Label')\n",
        "plt.show()\n",
        "\n",
        "# Plot training and validation metrics\n",
        "plt.figure(figsize=(16, 6))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        },
        "id": "hlAFa4HS5Hex",
        "outputId": "8e6c2354-be80-45bf-86ed-b9e3e7ae535a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 1600x600 with 0 Axes>"
            ]
          },
          "metadata": {},
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x600 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Accuracy plot\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(history.history['accuracy'], label='Train Accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
        "plt.legend()\n",
        "plt.title('Accuracy')\n",
        "\n",
        "# Loss plot\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(history.history['loss'], label='Train Loss')\n",
        "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
        "plt.legend()\n",
        "plt.title('Loss')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "v6wB04IX5xAr",
        "outputId": "c2ba1c1a-ead0-4f95-da30-7fb907917abe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the model\n",
        "model.save('/content/drive/MyDrive/ecg_transformer_multiclass_model.h5')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        },
        "id": "KTowZiVu57o9",
        "outputId": "8c732e35-d1f9-489d-c359-8c6eacac57a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'model' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-e6d1202753a4>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Save the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive/MyDrive/ecg_transformer_multiclass_model.h5'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "c5k5rNKU6BTC"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}